Harvard AI Professor: The People Most at Risk From AI Aren’t Who You Think

1. 0:00 – 2:07

   Why using AI isn’t enough

   1. KLKarim R. Lakhani0:00

      I have fired people who have told me, "The AI gave me that."

   2. MMMarina Mogilko0:03

      This is Karim Lakhani, a Harvard Business School professor. His research with BCG identified three ways people work with AI. Two groups developed their skills. One didn't. So how do you know which group you're in?

   3. KLKarim R. Lakhani0:17

      In terms of learning, self-automators do the worst because they have stopped thinking, they've stopped engaging in the activities. We can do way more complex things. We can push our expertise way further than before.

   4. MMMarina Mogilko0:28

      Can you give me some practical tips how I can train those assistants? What are the prompts?

   5. KLKarim R. Lakhani0:32

      I think the first is-

   6. MMMarina Mogilko0:36

      You are the author of this famous phrase, "You won't be replaced by AI, but humans with AI will replace humans without AI." And you helped launch the AI course every Harvard MBA student now has to take, and you also run these randomized experiments up, on thousands of workers at, at BCG, Procter & Gamble, and H&M. So today, I want to talk practical things-

   7. KLKarim R. Lakhani0:59

      Yeah

   8. MMMarina Mogilko0:59

      ... how people actually use AI and where it works and where it doesn't. Going back to your famous phrase, after your latest experiments, do you think it's tapping into replacing humans, or it's just humans with AI replacing humans without?

   9. KLKarim R. Lakhani1:12

      Look, I mean, I think this world is changing really fast in front of us. I still believe [laughs] in the foreseeable future, it's humans with AI. Like AI are not gonna independently start to figure out hypotheses and go solve those hypotheses. They need some direction. They need some, some curation. They need some taste on the way. A bunch of the execution tasks are getting done with the machines, but I think the... what we're in the middle of is a reconfiguration of both organizations and occupations at the same time. Um, if you sort of go back 100 years when modern production systems got invented, we started to create new companies and new organization forms, but also new occupations were being set up as well. I believe the same thing is happening right now with, with AI today, that we're s- g- in the middle of us figuring out what does work look like, what does occupation look like, and what does an organization look like. But the world of machines completely repl- replacing humans, I just don't, I just don't see it yet.

2. 2:07 – 4:24

   Cyborgs, centaurs, self-automators: 3 ways to use AI

   1. MMMarina Mogilko2:07

      Yeah. And you have this fascinating study where you studied 244 BCG consultants, and some of them were cyborgs, 60%-

   2. KLKarim R. Lakhani2:15

      Yes

   3. MMMarina Mogilko2:15

      ... meaning they were working together with AI. Then 14% centaurs, they split the work-

   4. KLKarim R. Lakhani2:21

      Yes

   5. MMMarina Mogilko2:21

      ... with AI for specific purposes. And then there were 27% self-automators-

   6. KLKarim R. Lakhani2:25

      Yes

   7. MMMarina Mogilko2:25

      ... that delegated everything and didn't learn anything.

   8. KLKarim R. Lakhani2:27

      Yes.

   9. MMMarina Mogilko2:28

      Can you talk to me about prompting? 'Cause I know a lot of people are worried they might be delegating too much, and some people are worried they're not delegating enough. What was the difference in prompting in these three groups?

   10. KLKarim R. Lakhani2:38

       So this was a part of a larger experiment at BCG, where we were able to get a- access to about 800 consultants, and we randomized them into different types of tasks and access to AI or not AI. What we observed very clearly was that those that got access to AI had three different genres of working. So cyborgs, they were intimately working with the AI and doing q- joint problem-solving. Centaurs, that separated themselves and said, "I'm good at X. The AI's good at Y. I'm gonna give, delegate some tasks to, to AI, but I'm gonna supervise when I get back." And the self-automators who press play. In terms of learning, the self-automators do the worst because they have stopped thinking, they've stopped engaging in the activities, and they are just basically... I can be harsh sometimes with my students. Like, if all you are is a meat interface to the silicon, what's, what's your value?

   11. MMMarina Mogilko3:26

       Yeah.

   12. KLKarim R. Lakhani3:26

       Right? And so it's easy to fall into that trap. Um, uh, one of my former postdocs who's a professor at Michigan now, Fabrizio Dell'acqua, did this great study showing people can fall asleep at the wheel with AI. Good people with good AI fall asleep. So I think self-automation is actually places where y- things can go bad quickly. I think the interesting part is gonna be one about cyborg and centaurs. And so cyborgs are this, the folks that are intimately figuring out what AI is good at and what they're good at, and they work this way. What I observe in R&D tasks, in sales tasks, in marketing tasks is that the, the cyborgs actually have a lot, lot to learn and a lot to figure out. And then the, the centaurs who divide the work, the worry you would have is that they stay static in terms of the capabilities of the AI and what they can do. And because the AI capabilities are, are improving exponentially, we have to be paying attention to the fact that are you updating the separation between yourselves and, a- and the machine

3. 4:24 – 9:00

   Why AI can sound convincing and still be wrong

   1. KLKarim R. Lakhani4:24

      as well?

   2. MMMarina Mogilko4:24

      And it gets really complicated, right? 'Cause you have this study about jagged frontier-

   3. KLKarim R. Lakhani4:29

      Yes

   4. MMMarina Mogilko4:30

      ... right? And I have the graph here-

   5. KLKarim R. Lakhani4:32

      Yes

   6. MMMarina Mogilko4:32

      ... where sometimes you think the frontier is fully capable of, uh, doing the task because it's inside.

   7. KLKarim R. Lakhani4:38

      Yes.

   8. MMMarina Mogilko4:38

      But then there is a similar task that seems as hard-

   9. KLKarim R. Lakhani4:41

      Yes

   10. MMMarina Mogilko4:41

       ... and then suddenly it's outside the frontier.

   11. KLKarim R. Lakhani4:43

       Yes.

   12. MMMarina Mogilko4:43

       Is there a way for a normal human to differentiate-

   13. KLKarim R. Lakhani4:46

       Yes

   14. MMMarina Mogilko4:46

       ... these tasks?

   15. KLKarim R. Lakhani4:47

       So I think this is such a great question, and by the way, like, this came from this, this deep study we did, did actually on BCG as well. And what was fun about it was that we had a bunch of hypotheses that AI is gonna be good at everything, and then we launch it, and we discover that in fact, no, like, for, for the job of a consultant, there are some things it's good at and some things it's not good at. The challenge for us now is we have this intelligence available to us, but the intelligence is not perfect, but it talks really well. [laughs]

   16. MMMarina Mogilko5:14

       Mm-hmm.

   17. KLKarim R. Lakhani5:15

       And it can be very persuasive.

   18. MMMarina Mogilko5:17

       Yeah.

   19. KLKarim R. Lakhani5:17

       Um, and so the question is: How do you apply your skeptical thinking and high standards on the things that the AI is actually doing and not fall asleep at the wheel, not fall in the trap of just believing what the AI says? This is gonna be an evergoing edge that I think humans will have to keep developing. Because the AI will keep getting better and better and better, at least for the foreseeable future, and we have to keep saying, "Under what circumstances does it break?" And the only way that actually happens, even in my own personal practice, is to be using the The most frontier tasks and, and doing the most frontier models along the way. That's the only way you'll actually discover, and not to sort of say, "I expect magic and rainbows and unicorns the first day." Engage with it and be critical about it along the way.

   20. MMMarina Mogilko6:00

       Yeah, and the problem is once you come to a conclusion like, "Oh, this task is good, this one is not," the next day [laughs] a new model comes out-

   21. KLKarim R. Lakhani6:06

       No, it-

   22. MMMarina Mogilko6:06

       ... and you have to reevaluate again.

   23. KLKarim R. Lakhani6:07

       Yeah, and so that's why, like, this constant change and the constant upgrading is something that we all just have to get comfortable with if we're going to be living in this world with AIs, and it's so damn difficult for all of us.

   24. MMMarina Mogilko6:16

       It is. It is.

   25. KLKarim R. Lakhani6:17

       Yeah. It is very difficult.

   26. MMMarina Mogilko6:18

       You know, we're so used to workflows-

   27. KLKarim R. Lakhani6:19

       Yes

   28. MMMarina Mogilko6:20

       ... and we thrive on routines, but [laughs] it's every day.

   29. KLKarim R. Lakhani6:22

       No, I think that's out the window now, unfortunately, and I think... But I think it's, it's actually, I think for us, as AI does more of the execution, it does raise the question, like, what are we really adding value for now? And so for me, I think it's a, it's a great place for humanity to be at, to sort of say, "Let's not be comfortable with where the w- what equilibria we're at now. Could we actually go, go solve bigger problems, go after bigger things in, in, uh, for humanity?"

   30. MMMarina Mogilko6:46

       That's a very interesting question because in your persuasion bombing study, when BCG consultants pushed back on GPT-4, it often argued even harder, even when it was wrong.
4. 9:00 – 11:49

   The prompts Karim uses to challenge AI

   1. KLKarim R. Lakhani9:00

      well.

   2. MMMarina Mogilko9:00

      Can you give me some practical tips on how I can train those, uh, assistants? What, what are the prompts?

   3. KLKarim R. Lakhani9:05

      I think the first is, like, like, don't believe what I'm saying either. Like, push back at me.

   4. MMMarina Mogilko9:11

      Mm-hmm.

   5. KLKarim R. Lakhani9:11

      Then it's like, uh, if, if a proposal is given in front of you, counterargue always. Like, like, give me a proposal and come up with three different arguments that would make this obvious or make this not non-relevant as another thing. The other one is, like, always do additional research. Like, you- you've got this thing, but go ahead and go additional research and, and find me red flags or find me, uh, um, the sort of the, the black swans that it may not be happening. And so it's these types of, uh... And depending on the task at hand, you can, you... Like, if it's a research agent, you can actually push today's models to do... They're gonna be thrifty in tokens, and you wanna actually, like, I'm always like, "Let's just squander tokens now-

   6. MMMarina Mogilko9:50

      Mm-hmm

   7. KLKarim R. Lakhani9:50

      ... in this space till we figure these things out," to actually go and go way more deeper than, than what the model will do itself, and don't ta- I never take the first answer.

   8. MMMarina Mogilko9:58

      Mm.

   9. KLKarim R. Lakhani9:58

      I never take the first answer, and either I've got the system prompt for it to go back and c- c- come up with alternatives, or I'll just push back myself.

   10. MMMarina Mogilko10:05

       I use AI for a lot of things now, but the annoying part is that I still have to decide where to do each thing. There is a model for research. Then I do writing in another one, images somewhere else. Video is in another tab, and then something new launches, which happens almost every single day, and suddenly there is one more subscription to keep track of. So I started testing ChatLLM by Abacus AI. I gave it a few completely different tasks. Research something for me, build a presentation, generate an image. And instead of me stopping every time to think, "Okay, which model should I use for this?" Route LLM just picks the model for the task. You still get access to the big ones, ChatGPT 6, Astra, Claude Fable 5.1, Gemini 3.5, plus Groq, DeepSeek, and others. But I don't really need to care which one is behind every prompt. It can also build a website or a full app and connect payments, generate realistic images and videos, create research presentations with charts, and run agents in the background. So instead of having an AI folder with 10 different tools in it, this is starting to feel more like one place where I can just get the work done, and it's $10 a month, which makes sense when you compare it with paying for all those subscriptions separately. If you want to try it, go to chatllm.abacus.ai. I'll leave the link in the description. Thanks to ChatLLM by Abacus AI for sponsoring this video. I was trying Google's Argon latest model.

   11. KLKarim R. Lakhani11:32

       Yeah.

   12. MMMarina Mogilko11:32

       So my whole process was, let's give AI data. Let's tell it how to use that data. Let's describe a workflow. What's happening with the new models, you just give them access to whatever data, and they figure out-

   13. KLKarim R. Lakhani11:45

       Yes

   14. MMMarina Mogilko11:46

       ... which data to use. Does that change the process you just described?

5. 11:49 – 15:48

   Why judgment and taste still matter

   1. KLKarim R. Lakhani11:49

      I think it does, but I think it's dangerous, so here's, here's my perspective. Not dangerous. I mean, I think I'm overstating it. I, I think it's worth paying attention to. What's happening inside of these models, right? They are regressing to the mean. In the end, it's a statistical model that is predicting the next word Overall, you, you know, everybody knows that that's in the audience here. But philosophically, what's going on is that if you let it choose the approach and you're not paying attention to the approach it's choosing, then the worry is that it's going to pick the average approach and not the one that's going to differentiate you or find you the answer. So many people have said, like prompting is becoming easier, models can prompt themselves. I go, "I get that," but foundationally, what's happening is that it's still going to go to the mean prompt and the average prompt, and the average prompts aren't going to be good enough for your particular use case. And so this is where I think we have a very interesting learning and growing part for humans in place. So the models we show in our research, the models allow you to do more and expand your role. Great. Now, the question is, as you expand your role, like we've heard, for example, that product development teams are becoming one-person teams, right? So the engineer can also do design, can also do, uh, user interviews, and can also do QA testing and because the AI helps you along the way. Great. But do you actually understand the, the principles of design and do you actually have a sense of taste? Because that's what designers do. They have a sense of taste. You know, my daughter did four years of industrial design. She has way better taste in products than I do, right? And the AI can provide you with some sense of design, but is that going to be enough to differentiate you or not?

   2. MMMarina Mogilko13:26

      Mm-hmm.

   3. KLKarim R. Lakhani13:27

      And so will you get the average design or will you get the one based on expertise? And there, the measure of taste will matter a ton because you will say, "Okay, like give me a design for this webpage," or, "Give me a design for this product," and you get the average response. And even saying, "Give me a Steve Jobs one," isn't enough. Like, there's a sense of taste. So the question I would always have is like you're better off like learning from the AI about what the elements of design need to be first, and then you make the decision on the genre you want to go down under or the approach of the design instead of, again, pressing play and getting it to do everything along. So to your question, you give a dataset, it can figure out five different ways to do regression analysis, you know, five different models. But I've had, you know, 20 years of research experience, five years of PhD training. Oftentimes, the models it picks aren't the right models to go after-

   4. MMMarina Mogilko14:16

      Mm-hmm

   5. KLKarim R. Lakhani14:16

      ... because, again, they're the average answers, not the ones for the task at hand. And this is where you can use the AI to help you educate, to learn, but also push it into three different targets of models and then be adversarial about it and say, "Prove myself wrong, prove itself wrong. Find me pros and cons along the way to get to the, the baseline that you're getting to."

   6. MMMarina Mogilko14:36

      The problem I see here, if I'm a one-person team, for example, and I want to use AI for a design, how do I measure AI's taste and how do-

   7. KLKarim R. Lakhani14:43

      Yeah

   8. MMMarina Mogilko14:43

      ... I understand whether it's a world-class work or just very, very mediocre?

   9. KLKarim R. Lakhani14:47

      Exactly, and that's where I think the responsibility doesn't go away because in the end, you're going to put your name. I have fired people who have told me, "Well, the AI gave me that." Again, are you a meat interface to AI? Then I don't need you.

   10. MMMarina Mogilko14:58

       Mm-hmm.

   11. KLKarim R. Lakhani14:58

       Right? You have to still-

   12. MMMarina Mogilko15:00

       Yeah

   13. KLKarim R. Lakhani15:00

       ... be engaged, and I think that's the, that's the really tricky part we're in the middle of and which is becoming-- it's important for me as a educator and as a researcher, I almost fall in that trap, but also for all of us because what we see is that it becomes so easy to cut and paste. It's so easy to do that, versus if you're going to be a one-person product team, then you need to be in the sense of, "Okay, I now need to build some knowledge about design, and where do I learn that from? How do I b-b-bring that up, and how do I set up my AI system so then, again, it's not just giving me the first design, but I can actually prompt it to go in different directions?" So the burden of learning and taste is back to the human.

   14. MMMarina Mogilko15:42

       Yeah.

   15. KLKarim R. Lakhani15:42

       Otherwise, we're going to be back in the phase of what you said. The machines will replace humans if humans stop exercising

6. 15:48 – 19:58

   How juniors can build experience with AI

   1. KLKarim R. Lakhani15:48

      their judgment.

   2. MMMarina Mogilko15:48

      What do you tell to your students when they ask you, "How do I learn taste if all of the basic tasks are already taken by AI?"

   3. KLKarim R. Lakhani15:56

      Yes.

   4. MMMarina Mogilko15:56

      Where is this place for, for the junior in the career ladder-

   5. KLKarim R. Lakhani15:59

      Yes

   6. MMMarina Mogilko16:00

      ... and how do they develop this taste?

   7. KLKarim R. Lakhani16:01

      Uh, look, again, I, I feel like I'm in the middle of that tsunami right now at, at Harvard Business School with, you know, 25-year-olds that come, 27-year-olds that come to our campus. I think there are three different perspectives I'll add to this, and I have more questions than I have answers, but I have heuristics on this. The first is you can imagine this career ladder story. I think the interesting question is are the bottom rungs of the career ladder being pulled out from juniors, or is the AI a springboard for you to go higher?

   8. MMMarina Mogilko16:29

      Mm.

   9. KLKarim R. Lakhani16:29

      So my good colleague Mitch Weiss has sort of said he feels like this is a springboard and not just a removal.

   10. MMMarina Mogilko16:35

       But if taste is required-

   11. KLKarim R. Lakhani16:37

       Yes. So let's get there

   12. MMMarina Mogilko16:37

       ... it's there.

   13. KLKarim R. Lakhani16:38

       Right. Exactly, right.

   14. MMMarina Mogilko16:38

       Yeah.

   15. KLKarim R. Lakhani16:38

       So, so then it's like, okay, it's a springboard. So then the question becomes how do you as a junior person develop the taste to design the best investment banking analysis, design the best consulting study, design the best product, design the best customer experience, and so forth, right? That's where the burden of learning becomes even heavier on you. That's where you have to sort of take responsibility for the learning, where you say, "If now I can do so many different things, what are the fu-foundational pieces that I need to understand so I can learn to push back on this?" And this is where my second aha has been. So I just watched this great, uh, YouTube conversation with Stephen Wolfram, who you might want to actually invite on your show. And in this interview, he said something that was, that has just, uh, I heard it like 10 days ago, and it's just been like rattling in my brain. You know, he designed mathematical languages to do integrals. And so he goes like, "You don't need to do integrals anymore by hand. You can just use integrals." But he saw his kids in school learning integrals, and he's like, "Why? I designed this, you know, decades ago. Why now are you learning integrals?" And what his story was, which is actually super interesting, was that, look, you need to learn to do integrals by hand initially just so you understand how the things work. But the next 50 integrals or the next 100 integrals, you should let the machine do it because actually, if you observe 1,000 integrals being done by the machine, you will learn a lot more about integrals than you doing the next 40-

   16. MMMarina Mogilko18:02

       Mm

   17. KLKarim R. Lakhani18:02

       ... by hand, the next, uh, 60 by hand, the next 80 by hand. And so what is interesting there for me, I, I think this is a thesis about how do we deal with this automation, which is a foundational piece you need to learn and do by yourself first. But then you can give to the machine to do the rest, and then you are now an observer of the underlying patterns that are becoming true to make this happen. And so I think- For me, as we're thinking about this, this role expansion story, it behooves us when we're gonna be responsible for these things to understand the fundamentals enough to be able to then use the machines in the proper way.

   18. MMMarina Mogilko18:39

       Yeah.

   19. KLKarim R. Lakhani18:39

       And so it, it, it... And again, I'm, it's in a bit of ways, I, self-centered because I'm an educator. I say people just need to keep learning.

   20. MMMarina Mogilko18:46

       [laughs]

   21. KLKarim R. Lakhani18:47

       I, I think the learning side keeps increasing in importance because it's no longer what I just learned in school or what I learned in university. It's gonna be this continual things. As capabilities expand of these models and we're gonna use them for our things, we still have to understand the foundations pieces to be able to push forward.

   22. MMMarina Mogilko19:04

       Yeah, so basically, I see two challenges here. First challenge, don't jump to the machine too fast-

   23. KLKarim R. Lakhani19:10

       Yes

   24. MMMarina Mogilko19:10

       ... because what we see with kids, they're just using ChatGPT for homework-

   25. KLKarim R. Lakhani19:13

       Yeah

   26. MMMarina Mogilko19:13

       ... and you probably see it in specialists, right? Just like-

   27. KLKarim R. Lakhani19:15

       We've seen studies, totally.

   28. MMMarina Mogilko19:16

       Mm.

   29. KLKarim R. Lakhani19:16

       Like, that, that, that's... The cutting and pasting, you don't learn anything at all.

   30. MMMarina Mogilko19:20

       Yeah. And the, the second, not danger, but challenge, you have to learn so much more in four years at college-
7. 19:58 – 23:21

   What Harvard MBA students now have to build

   1. KLKarim R. Lakhani19:58

      capabilities.

   2. MMMarina Mogilko19:58

      If you compare this year's students versus students two years ago, are the students now learning much more than they used to a couple years ago?

   3. KLKarim R. Lakhani20:06

      Yeah. So look, in my MBA class, we, you know, we have a required course in AI, so everybody has to do it, and we've had to pivot the cour- It's three years old now, and every year we have to pivot the course-

   4. MMMarina Mogilko20:16

      Or every week [laughs]

   5. KLKarim R. Lakhani20:17

      ... because AI is... Yeah, exactly. In the, in the middle of teaching, we have to change stuff.

   6. MMMarina Mogilko20:20

      Yeah.

   7. KLKarim R. Lakhani20:20

      And so one of our big challenges was, like, last year, the, the clause showed up all of a sudden, and we're like, "If we don't talk about clause in our, in our, in our course, we're dead. Like, we won't have any legitimacy." So how do we pivot rapidly to teach about clause and so forth? The aha moment for us was to say, "We can't just talk about AI in business school. We have to do AI." So we've done this radical shift in our class where half the class now coming, we're gonna teach again in the, in the spring, is gonna be building with AI.

   8. MMMarina Mogilko20:49

      Mm.

   9. KLKarim R. Lakhani20:49

      Uh, and so they actually have to learn to solve business problems with AI and build advanced AI tools. So they're gonna be building RAGs. They're gonna be doing evals. They're gonna be building multi-agent systems. They're gonna be building, you know, whole ranges of things to solve business problems and to understand the nature of the technology itself and how it's changing. We had to throw out our final exam because how can we legitimately have a course on AI and let them not use a, an AI tool to do the final exam? So what we said is, "You actually have to now build projects. So you have to vibe code projects." So we had all 900 of our MBA students vibe code solutions, and some of them were spectacular. Like, I was blown away for what the students were able to build. So we pushed a ton of building as part of the AI curriculum, more to basically sort of push them to say, "These capabilities are such where the only way you will actually understand what the, w- what the AI's impact on your job and your company and your future life is gonna be if you are fully immersed in it and you, you learn how to get the most out of the tools." But at the same time, we push the foundations of, like, what are the economics behind it? What's the strategy behind it? What does the change management part look like? And we, we couple these exercises with the cases and the discussions to really get them to sort of see, AI now can do all these magical things, but then what's the implication for me in terms of a company or a strategy or a job that I sort of see? And so that's been, for me, super challenging-

   10. MMMarina Mogilko22:17

       Mm-hmm

   11. KLKarim R. Lakhani22:17

       ... because, you know, for, for 17 years I was just happy to just put a case in front of the students and go, and now we- we're, we're doing a lot of doing in the classroom. But also, uh, it's a blessing because it really pushes me to the bleeding edge to sort of see what, where the edges are for both myself and our students on what needs to happen.

   12. MMMarina Mogilko22:35

       When you say this, this really want, uh, makes me want to take the course.

   13. KLKarim R. Lakhani22:38

       Yeah.

   14. MMMarina Mogilko22:38

       Is there a way for someone outside Harvard Business School to take it or-

   15. KLKarim R. Lakhani22:42

       Um, we are still sort of figuring that out. We do have some online courses. We have AI for leaders. We have, um, uh, A- AI Essentials for Business that are being developed. They're online, but not to the depth of what-

   16. MMMarina Mogilko22:53

       Mm

   17. KLKarim R. Lakhani22:53

       ... what we do when you are on campus with us and we're kicking butt with our students. [laughs]

   18. MMMarina Mogilko22:57

       I think everybody needs something like that-

   19. KLKarim R. Lakhani22:59

       Yeah, yeah

   20. MMMarina Mogilko22:59

       ... 'cause everyone's going through this. Every week on this podcast, I talk to AI founders, entrepreneurs, and experts who actually build AI and ask them their secrets, prompts, and actual workflows for using AI in real life. I put them and my own use cases into my free newsletter. It's called Future Proof. Subscribe using the link in the description and get my empty AI writing guide as a thank you.

8. 23:21 – 28:30

   Why CEOs need to use AI themselves

   1. KLKarim R. Lakhani23:21

      When we do executive activations, for example, with C-suites, it's funny, like, every C-suite, you know, chief of staff or executive member says, "Oh, we're already doing lots of AI. We already know this." And I'm like, "That's total BS." Like, I don't believe any of it, right? "No, no, we've got it covered."

   2. MMMarina Mogilko23:34

      Mm-hmm.

   3. KLKarim R. Lakhani23:34

      I go, "Okay, whatever." But then you have to do AI for business, and I want the entire C-suite vibe coding, and not just trivial applications, but applications for themselves in their business.

   4. MMMarina Mogilko23:46

      Mm-hmm.

   5. KLKarim R. Lakhani23:47

      Right? Like, don't make me, like, a sports betting app, but tell me if you're running production and operations inside of a major global conglomerate, what are the challenges you face and how would vibe coding a solution matter? There's always resistance. Like, oh, the, the-

   6. MMMarina Mogilko23:59

      The IT department [laughs]

   7. KLKarim R. Lakhani24:00

      ... both the, the IT depart-

   8. MMMarina Mogilko24:01

      [laughs]

   9. KLKarim R. Lakhani24:01

      Oh, don't even start with the IT department.

   10. MMMarina Mogilko24:02

       [laughs]

   11. KLKarim R. Lakhani24:03

       You guys, oof. Once we have the ear of the CEO, they say, "Look, just bring this in and just try... Let's just... It's just a trial." And what happens is in most large organizations that I've encountered, the C-suite and the senior leaders have, have, have been cushioned from the realities of what the models can do and what kind of work can happen. And so they have this third-hand view about what the AIs can actually do. But, and when we put them in front of keyboard to create an application in 90 minutes, all of a sudden their brains go on fire because two things happen. First, they yell at the CIO saying, like-

   12. MMMarina Mogilko24:39

       Mm-hmm

   13. KLKarim R. Lakhani24:39

       ... "I've been asking for this for bloody five years."

   14. MMMarina Mogilko24:41

       Yes. [laughs]

   15. KLKarim R. Lakhani24:41

       All of a sudden I built it.

   16. MMMarina Mogilko24:42

       Mm-hmm.

   17. KLKarim R. Lakhani24:42

       And the CIO has all these ex- explanations for it. But secondly, the aha is, "Oh, if I'm not doing AI, how can I guide my teams to do AI as well?" If Programming is the leading indicator of what's gonna happen in the world of AI. Every other occupation is six months to two years behind. So now I have a sense of what is happening in one occupation, but finance, marketing, supply chain, sales, they're all gonna get transformed in, in this way, and we're at this precipice moment for us as leaders in these companies to actually do the AI, to be able to fill that, uh - to, to be able to put that through. So then we force the doing as part of the executive activations that we do. And every single time, there's so much resistance against it, and every single time they come back and say, "Thank God we did this," because now I have a much better appreciation of what, what needs to happen along the way.

   18. MMMarina Mogilko25:36

       From what you're seeing, how far behind are these companies in terms of AI? 'Cause we're-- I'm in Silicon Valley bubble.

   19. KLKarim R. Lakhani25:41

       Yes.

   20. MMMarina Mogilko25:41

       Everyone is-

   21. KLKarim R. Lakhani25:42

       All you guys are-

   22. MMMarina Mogilko25:42

       ... coding-

   23. KLKarim R. Lakhani25:42

       Yeah, that's probably-

   24. MMMarina Mogilko25:42

       ... everyone's building.

   25. KLKarim R. Lakhani25:43

       Yes.

   26. MMMarina Mogilko25:44

       But I'm talking to my friends who are building AI companies, and they say, "We hold conferences on very basic things, and people are so excited." How far behind are those executives in corporations?

   27. KLKarim R. Lakhani25:54

       I think, I think, you know, there's a William Gibson saying, "The future is already here -

   28. MMMarina Mogilko25:57

       Mm-hmm

   29. KLKarim R. Lakhani25:57

       ... it's just not evenly distributed." It's the case here for sure. I think Silicon Valley and other, other pockets in the world are living in a very different future than most companies. To be quite honest, I would say there are two things to pay attention to. Most companies are probably at GPT 4.5.

   30. MMMarina Mogilko26:14

       It's basically search, advanced search.
9. 28:30 – 34:40

   Set bigger business goals – then bring in AI

   1. KLKarim R. Lakhani28:30

      on the change side.

   2. MMMarina Mogilko28:31

      What's the way to solve it? Does it have to come from C-suite, or is there-

   3. KLKarim R. Lakhani28:34

      Oh, yeah

   4. MMMarina Mogilko28:34

      ... something an individual can do?

   5. KLKarim R. Lakhani28:35

      No, I, I, I think, I think the, the individual responsibility is, like, gosh, if you're on the travelator, you better figure out what, what practices you need to be doing to get up on the-

   6. MMMarina Mogilko28:44

      As soon as possible

   7. KLKarim R. Lakhani28:44

      ... on the thing. And it's not an escalator, as you probably learned. Like, just because agentic is available to you today, if you haven't been through the cycles of prompting and learning the models and, and knowing where it breaks and the, the tastes you've built and the heuristics you've built on how to use these models, you, you have to go on this journey yourself. So accelerating that journey-

   8. MMMarina Mogilko29:03

      Yeah

   9. KLKarim R. Lakhani29:03

      ... will matter a ton personally. But from the large company perspective, it actually has to be top-down. They have to set, uh, targets around this, not performative targets of AI. I often say that what you have to do is you have to reimagine your business, value creation and value capture for your business, and your operating model. Um, but the, the main thing is to targets that are actually about business results. How, how more effective you want to be in terms of serving your customers, how more effective you want to be in terms of your R&D productivity, how more-- in terms of new product launches, how more effective you want to be in your operations side, for example. And set almost unreasonable targets in short amounts of time, and make those targets business targets and customer targets, and then say: How does AI help us get there? 'Cause then that enables the company to actually be able to, to then align around, "Of all the million things I could be doing with AI, where should I be focusing my attention?"

   10. MMMarina Mogilko30:02

       This is such a great question when it comes to targets and KPIs and AI impact-

   11. KLKarim R. Lakhani30:07

       Yeah

   12. MMMarina Mogilko30:07

       ... on those, because sometimes the target that you set doesn't really reflect the AI's input alone. Maybe there are other factors happening.

   13. KLKarim R. Lakhani30:17

       Yes.

   14. MMMarina Mogilko30:18

       Do you have any frameworks around that, even, like, for a small organization?

   15. KLKarim R. Lakhani30:23

       So look, I, I think, I, I almost feel like we need to take a step back and imagine this world of exponential improvement in model capability. The trick for us is to say: How do I keep up with the new capabilities being unlocked on a almost a daily basis now or weekly basis now to my essential business, right? And so in a business perspective, even a small business, one of the things I've learned in studying business and teaching about business and doing research is that targets are sort of- Acts of imagination. And we can have little imagination, or we can have a lot of imagination. One of the enduring facts is that when leaders set outlandish targets, which seem ridiculous, which seem kinda scary, which seem unachievable, sometimes they... most of the times they actually come true. Because if you just set an average target for yourself-

   16. MMMarina Mogilko31:14

       Mm-hmm

   17. KLKarim R. Lakhani31:14

       ... guess what? You'll probably be below average by the time you achieve it, and there'll be many excuses to get there. But if you set, like, a target of, like, 10X, and you achieve 7X instead of 10X, but you achieve 7X versus achieving only 2X, that changes the world. So I think obligation for leaders to use their imaginations and push the organizations. People are looking for leaders to set the gu- uh, to goalposts, and then create the atmosphere to get there. And I f- really feel today with what AIs can do. The point is, the models keep getting better and better and better. You can use these targets. A- again, these are not AI targets. These are business results targets, revenue increase and so forth. And then work your way backwards. If this was gonna be true, what else needs to be true?

   18. MMMarina Mogilko32:00

       What do we need?

   19. KLKarim R. Lakhani32:00

       What else do we need to be true, and what else needs to be true?

   20. MMMarina Mogilko32:02

       Before we keep going, I'd love to get to know you a little better. I share a lot about my work here, and I'm curious about yours. What do you do, and what you'd like more help with? We've put together a little survey. Your answers will help me decide what to cover in future conversations and what resources to put together for you. I'll leave the link in the description. Please, please, please take a couple minutes and tell me about you, about your business, about your ambition, so I can make better content for you. I really, really appreciate you. Thank you so much for doing this. Yeah, it's the ambition question, right? And when you... Uh, and this is interesting. I'm just, uh, remembering another study you did, uh, for H&M.

   21. KLKarim R. Lakhani32:42

       Yes.

   22. MMMarina Mogilko32:42

       And when AI created the solution, it was really hard for people to sell that, which kind of means that you can't use AI to work on your ambition, right? 'Cause if AI sets a strategy for you, you won't be able to sell it to your-

   23. KLKarim R. Lakhani32:55

       100%. 100%. And so this is where, again, your... You have to push first, and the AI can help you then bolster it. It can't be the other way around.

   24. MMMarina Mogilko33:05

       Okay, how do you teach your students to, uh, become more ambitious? [laughs]

   25. KLKarim R. Lakhani33:08

       [laughs]

   26. MMMarina Mogilko33:08

       That's the question, right?

   27. KLKarim R. Lakhani33:10

       Look, there's a normal distribution of ambition as well in the world, and I think what we want is that for that normal distribution to move more to the right so people can be more ambitious. It gets deeply personal, you know, because it's like people have fear of failure. People have all these worries about what other people will think and so forth. And of course, we, we live in a social setting. We wo- worry about what other people think. But ambitious people care about something in the world that they wanna change and are committed to it, and then will find ways to get others behind them, um, and set big targets. And often worry I have often at Harvard Business School with our MBA students is that they're... They've come from a setting where they've always been successful. To get into HBS, you had to have a impeccable CV, impeccable grades, and so forth. And, and so if you haven't encountered enough failures, then you'll be like, "Well, if I do this, if this is, like, you're asking me to set a 10X goal, the mean outcome might be failure."

   28. MMMarina Mogilko34:05

       Yeah.

   29. KLKarim R. Lakhani34:05

       "I don't wanna do it, so I'll set a 2X goal instead or a half X goal instead, and that might be good enough." And so we always have to sort of bolster our students to say, "Look, like, the... You will always be fine. You've been to the top schools. You're a Harvard graduate. You'll be fine. Have faith in yourself, but set some, some incredible goals for yourselves and for the world. And you have the privilege to be at the, one of the best schools in the world, so let's use that for, for, for real good stuff instead of the average outcomes along the way." And so that's a constant for us as our faculty to keep pushing our students in that

10. 34:40 – 38:36

    What happens when AI makes expertise widely available?

    1. KLKarim R. Lakhani34:40

       direction.

    2. MMMarina Mogilko34:40

       Yeah. And I think thinking about AI as a team of experts-

    3. KLKarim R. Lakhani34:44

       Yes

    4. MMMarina Mogilko34:44

       ... that you have to help you achieve your goals-

    5. KLKarim R. Lakhani34:46

       Yes

    6. MMMarina Mogilko34:46

       ... is really empowering. But something you said made me a little bit scared. Um, the marginal cost of expertise is going to zero.

    7. KLKarim R. Lakhani34:54

       Mm-hmm. Mm-hmm.

    8. MMMarina Mogilko34:54

       Can you talk about what that means for knowledge workers?

    9. KLKarim R. Lakhani34:57

       Yes, 100%. So look, th- this is... I'm obsessed with, and in many ways, that's the thing that we're trying to figure out both at our institute and, uh, I have my own lab, the Laboratory for Innovation Science. At core, I'm still a nerdy researcher. I write obscure papers read by obscure people, [laughs] you know, uh-

    10. MMMarina Mogilko35:13

        Your papers are amazing.

    11. KLKarim R. Lakhani35:14

        [laughs] Thanks. Um, and I really wanna get to, like, show me data, let, let me run the experiment, let me get causal about it instead of just, like, waving my hands and saying the world is ending or the world is all utopia. And by the way, the way I got into this, and I'll connect it back to what you said, is that I became an academic to actually understand open source software, like, in the late '90s. Open source software was just taking off, and it made no economic sense. Why would developers who are highly paid, who are rare in the world, highly needed, spend all their time writing free code?

    12. MMMarina Mogilko35:44

        Mm-hmm.

    13. KLKarim R. Lakhani35:44

        Like, it made no sense. And what we figured out was that, in fact, what was happening with open source was there's both a incentives and motivation story, and there was a clear information economic story. W- what happened? Well, on the incentives and motivation story, people aren't just motivated by money. They're motivated by the fun of problem-solving. They're motivated by r- the, the, the, the social signals they might, uh, accumulate and so on and so forth, and those are not correlated with skill. So we could show that there is, there's raw skill which is not correlated with different, dif- differentiating motivations, and that explained why open source was working so well. People had different motivations to do things. And the second thing that we had, which was this, this, this phrase that Eric Raymond had said about open source, that given enough eyeballs, all bugs are shallow. Meaning that if enough people are attacking the problem, right, you will find the solution, and that's what open source was in many ways. And we sh- we, we showed that c- clearly. Then when this AI sh- shows up as, in many ways, an absorption of human expertise- We basically have this open source model at scale because what you observe is that if you-- the reason why open source works really well, if you imagine a normal distribution of skills, right, or normal distribution of outcomes, what open source relies on is that you have multiple shots on goal. Many different people are trying many different things, and you can take the best performing solution and make it available. So open source, um, innovation contests, crowdsourcing works on the basis of the, that the normal distribution is available to you. Most companies just get the average.

    14. MMMarina Mogilko37:15

        Mm-hmm.

    15. KLKarim R. Lakhani37:15

        In open source, you get the normal distribution, and you can get the right tail.

    16. MMMarina Mogilko37:19

        Yeah.

    17. KLKarim R. Lakhani37:19

        That's the right tail story. Let's bring this now into the world of AI. What AI is doing is basically shifting the, the, the human plus AI expertise curve to the right. Just again, imagine a normal distribution, and it's basically shifting it to the right. Wow, that's incredible because now all of us have th-these superpowers available. So our belief has been, and we've sort of seen this in our empirical studies at P&G, at H&M, at BCG, has been this view that you are just basically drawing from a distribution of expertise that the AI provides. This absorption of expertise is such that we all now need to understand, what have I developed myself individually? What does the AI have? And what's the differentiation of me versus AI? It can't not just be you without AI. It has to be always you with AI, and how are you better than just what the AI does by? I think that's gonna be the edge that I think we all need to figure out. In CAD/CAM design, I learned drafting by hand, and you can only do so much drafting by hand until CAD/CAM came about, and you could do way more complex things. And I think that's the way we need to think about AI. We can do way more complex things. We can push our expertise way further than before, and that... But if you don't, then the machine will replace you.

11. 38:36 – 41:15

    Expert with AI: why grind beats talent

    1. MMMarina Mogilko38:36

       Which kind of makes me think that you either do something where you think you're gonna be a top expert or you don't do it at all. Is that correct?

    2. KLKarim R. Lakhani38:44

       Expert with AI.

    3. MMMarina Mogilko38:45

       Expert with AI. But yeah, if you're not top of the field, you can't compete with AI, meaning you don't go there at all? Like, how do you make decisions about your future? [chuckles]

    4. KLKarim R. Lakhani38:53

       Yeah, yeah, no, good, good question. I mean, I think-

    5. MMMarina Mogilko38:54

       How do you, how do you know if you're gonna be great at something? That's, that's-

    6. KLKarim R. Lakhani38:58

       Do the work

    7. MMMarina Mogilko38:58

       ... that's the question.

    8. KLKarim R. Lakhani38:58

       Do the work.

    9. MMMarina Mogilko38:59

       All right.

    10. KLKarim R. Lakhani38:59

        Do the work. It is really grinding it out. And, like, I, I just feel like oftentimes grind always outperforms talent.

    11. MMMarina Mogilko39:06

        Oh, I love that.

    12. KLKarim R. Lakhani39:07

        Grind always outper- And now with AI, you can grind more, and you can learn more. And I think-- I just feel like, I think people settle too early. Even our best students settle too early, and we have to keep reminding them. I've been vacillating about this. You know, I, you know, I, I finished my undergrad in the last century, '93, and the internet was showing up. And then we've been through the waves of the internet, mobile, machine learning, and so forth. And the question I keep asking myself, is what we're seeing a difference of degree or a difference of kind, right? Is this just the next wave of internet, blah, blah, blah, or is this-- and compute or is it a difference of kind? I feel like I keep landing on this is a difference of kind. If it's a difference of kind, then all of us have to now keep thinking a lot about, what is my expertise? How am I developing this expertise? And how does my expertise change with AI? And that's an experiential good. It's not something that I can just teach you. You have to keep working at these tools and keep figuring out how to do that. I see this, like, my wife's an artist, my daughter is a designer, and I see them struggling with this themselves in their work to say, how does an artist now use AI to help them go further than before? Um, we've seen periods where technology has revolutionized the arts. You think about hip-hop, right? That's all about technology. Like, two turntables and the mixer. New forms of expression. And, and if you just think about how much hip-hop has taken over the world from, you know, French hip-hop to Farsi hip-hop to Chinese hip-hop, you know, to Japanese hip-hop to, uh, sort of the hip-hop here, that's because of technology, right? And so technology changed the art form, and I think this is across the board in all professions, in all occupations. And so you can't be a hip-hop artist without you mastering the two turntables or having people around you that know how to master the turntables and you know how to, how to, how best to use them. The turntables have become digital now, of course, right, and so forth. But I think that's the precipice we're at, and it's a difference of kind than business as usual, uh, that we've had, we've

12. 41:15 – 43:39

    Will AI agents decide what we watch?

    1. KLKarim R. Lakhani41:15

       seen.

    2. MMMarina Mogilko41:15

       Because you do drew a parallel with creators. As a creator, the next question for me is, how much can people consume?

    3. KLKarim R. Lakhani41:21

       Yes. Yes.

    4. MMMarina Mogilko41:22

       If we're developing new forms of art when it comes to business, if organizations become much more efficient and they're producing so much output, do you think we have capacity to consume all of that?

    5. KLKarim R. Lakhani41:32

       This is such a cool question you're asking me because... And again, I don't have an answer. I just, I'm, have questions. I'm, like, I'm, like, trying to figure out, like, what the right study is on this 'cause I'm always like, in the end, like-

    6. MMMarina Mogilko41:40

       [chuckles]

    7. KLKarim R. Lakhani41:41

       ... I'll make up some crap.

    8. MMMarina Mogilko41:42

       Making studies. [chuckles]

    9. KLKarim R. Lakhani41:42

       I need, I need-- Sorry, it's just my-

    10. MMMarina Mogilko41:44

        You're right

    11. KLKarim R. Lakhani41:44

        ... it's my tick. I just always end up there. Again, let's go back to software where, again, we see this bleeding edge. The-- If all of a sudden you have a doubling of the number of PRs you can push in your production, in your organization, soon human review is gonna be impossible.

    12. MMMarina Mogilko42:01

        Oh, it's still already being replaced by another model-

    13. KLKarim R. Lakhani42:03

        Exactly

    14. MMMarina Mogilko42:03

        ... stepping in and doing-

    15. KLKarim R. Lakhani42:04

        Exactly

    16. MMMarina Mogilko42:04

        ... the review, right?

    17. KLKarim R. Lakhani42:05

        Exactly. So now the question becomes... It goes back to what you asked, which is like, well, what does a human now do? What does a human now do? Because before, and I saw this in my open source studies, one of the reasons why people love to do open source is because they love coding. They love the grind of coding. I was a terrible coder, so I could never get into it, never understood it, but other people just do it. Now, when you take that away, where does meaning come from in your work? Now you're in this world of, like, is, is it all gonna be about managing agents? Well, that may or may not be meaningful, and I think we're in the middle of all of us in all of these occupations trying to figure out something new. We don't have the answers yet, and that's why it feels so uncertain and so, like, we all feel so unmoored along the way. But to your question about how do I consume more, well, there are two answers. One is that The models will be consuming our outputs. So part of what you will be thinking about is, like, how many MD files are you also pushing out, as much as you're pushing out all the content that you're doing? And, and if the models become... If I have my personal agent that is-- it's not gonna watch your video. It's gonna read your MD file and then say, "Go pay attention to these five segments in your show." Right? And so that's the-- I think that's part of the transition-

    18. MMMarina Mogilko43:20

        Yeah, but I do care about human views, though, right?

    19. KLKarim R. Lakhani43:21

        Oh, I know. Of course you do.

    20. MMMarina Mogilko43:22

        Yeah.

    21. KLKarim R. Lakhani43:22

        But I think, but I think it's now gonna be mediated through these tools.

    22. MMMarina Mogilko43:25

        Mm-hmm.

    23. KLKarim R. Lakhani43:25

        And so the question becomes: how do you do both, and how do you make them both available? Because you will care about human views, but if I will now be relying on my personal agent to tell me what to watch-

    24. MMMarina Mogilko43:35

        Yeah

    25. KLKarim R. Lakhani43:35

        ... and where to watch-

    26. MMMarina Mogilko43:35

        How do you discover me? Yeah

    27. KLKarim R. Lakhani43:36

        ... and how do I discover you, it becomes, it becomes important.

13. 43:39 – 45:37

    Are middle managers more at risk than juniors?

    1. MMMarina Mogilko43:39

       Yeah, such an interesting future. If you had to draw a picture of 2030, right?

    2. KLKarim R. Lakhani43:44

       Oh, my goodness. [laughs]

    3. MMMarina Mogilko43:45

       Just, just, like, trying to understand-

    4. KLKarim R. Lakhani43:47

       Yeah

    5. MMMarina Mogilko43:47

       ... with, with everything that we said. I didn't mention, you had another fascinating study where people were working with AI, and, uh, the output of a person with AI equaled a two-person team output.

    6. KLKarim R. Lakhani43:57

       Yeah, a team.

    7. MMMarina Mogilko43:57

       Right?

    8. KLKarim R. Lakhani43:57

       Exactly, yeah. This is our Procter & Gamble study, yeah.

    9. MMMarina Mogilko43:59

       When this happens, when junior work is getting automated, in 2030, what does an organization pay for?

    10. KLKarim R. Lakhani44:07

        So let me step back first. I don't think junior work is being automated, so I, I don't... I, I, I don't think that question is settled-

    11. MMMarina Mogilko44:13

        Mm-hmm

    12. KLKarim R. Lakhani44:13

        ... to be quite honest.

    13. MMMarina Mogilko44:14

        Okay.

    14. KLKarim R. Lakhani44:14

        Um, if anything, people that may be at risk are more in the middle management layers than in the junior layers, from my perspective.

    15. MMMarina Mogilko44:21

        Mm.

    16. KLKarim R. Lakhani44:22

        Here's why. The junior people are fearless to absorbing AI and doing stuff. You've probably seen-- I've seen high schoolers and undergrads. I don't see undergrads routinely as part of my, uh, graduate school work at, at, at HBS, but when I encounter them, I'm like, "Wow, you guys are a different set of human beings that are able to whip up all these things so quickly." So I would contend, and again, it's easy for us to say soft- There's multi-causal. The causal density's high in terms of why juniors, why the, why the early career market is soft right now. It may not just be AI. And my belief is that, in fact, the middle layer is more at risk than the junior layer. Why? 'Cause I, I observe inside of companies AI usage, and the middle layer is the one that's resisting the mo-most, doesn't wanna change. So it's the 35-year-olds to the 55-year-olds that are pushing back against AI usage and not changing, versus the more enlightened 55-year-olds, and again, the K-curve people, with the junior people, could run circles around most of the organization. So I think that's the first thing to say. Like, I don't think it's settled at all. Other colleagues of mine, esteemed colleagues, are worried about the junior side. I'm like, "If you hit real organizations and see real workers with real data, you would be skeptical about if this is the case."

    17. MMMarina Mogilko45:35

        But it's, it depends on the

14. 45:37 – 48:45

    Walmart, Kmart or Bezos: the question for every company

    1. MMMarina Mogilko45:37

       junior, right?

    2. KLKarim R. Lakhani45:37

       Well, exactly, right? So if it... Again, this is the pushback to the junior people. If all you are is a copy-paste meat interface to the model-

    3. MMMarina Mogilko45:44

       That job is gone, right?

    4. KLKarim R. Lakhani45:45

       That job is gone.

    5. MMMarina Mogilko45:46

       Yeah.

    6. KLKarim R. Lakhani45:46

       That job is completely gone.

    7. MMMarina Mogilko45:48

       Mm-hmm.

    8. KLKarim R. Lakhani45:48

       And by the way, that's the same question I ask law firms and every other CEO. I go, "If all you are gonna be is a thin wrapper around Anthropic at a law firm, or you're gonna be a thin wrapper around OpenAI's model for a manufacturing firm, why should you exist?" And that is very challenging for them to actually hear about. And so the question I ask is: how do we redefine jobs, and what does the nature of work now look like? Even in your setting, you could potentially be doing 10X more if you deployed AI even more at scale than what you're doing right now. And who are the right people to bring you along in that journey? Because you yourself can't do it all yourselves. And I think that's the question to be asking. So to your 2030 question, I still feel that there's gonna be a, a Tale of Two Cities. I think there's gonna be a set of firms that get it, that understand it, and they'll be both startups and large enterprises that do the heavy lifting to redefine the work. One of my colleagues, Rem Koning, has built up, uh, uh, this frontier firm radar, where he's looking at startups and their AI usage, and he's actually running experiments into actually showing them how to use AI better. And when he does that, they find faster ARR, faster launches of products, and so forth. So even with startups, there is a learning curve of how to use AI better. And so I believe that there'll be a set of firms and a set of founders and a set of senior managers and people inside the firms that get it and start to accelerate. And then I think there'll be everybody else, and they will be plodding along. And the only question in my mind by 2030 is, if you, again, go back to the late '90s and Amazon being established, uh, and retailers sort of laughing at Amazon, saying, "Oh, how cute."

    9. MMMarina Mogilko47:33

       Mm.

    10. KLKarim R. Lakhani47:33

        Right? But Bezos rethought retail commerce inside out-

    11. MMMarina Mogilko47:39

        Mm

    12. KLKarim R. Lakhani47:39

        ... versus Kmart and Sears just added a website to their database and weren't able to adjust. And w- and Walmart struggled for a long time, then figured it out, and is now as competitive as before. Um, so the question I have for incumbent firms is: are you gonna be Walmart, or are you gonna be Kmart now that the cost of intelligence, the cost of expertise, has gone down to zero? Companies are nothing but bundles of expertise, and if that cost has gone down to zero, what do you do now, and how do you set yourself up now for your business? And for incumbents, it's really, you know, you have two, two futures ahead: a Kmart or a Walmart view, uh, versus the Bezos. And the question is: who's, who are gonna be the Bezos in all these industries? 'Cause I think all of them are up for, for grabs, in professional services, in manufacturing, in consumer. The value destruction that people are anticipating, uh, uh, quickly, I, I think will take longer than, than m- just because, again, I sort of see inertia, uh, in, in so many organizations and inertia in customer behavior, in buying behavior, and so forth, that it just doesn't-

    13. MMMarina Mogilko48:42

        Yeah, we're still humans. We're not machines.

    14. KLKarim R. Lakhani48:44

        Yeah, exactly.

15. 48:45 – 49:14

    Will the next AI model strengthen your business?

    1. KLKarim R. Lakhani48:45

       Exactly.

    2. MMMarina Mogilko48:45

       Okay, well, I guess the, the last philosophical question is, how do you determine that you're not a wrapper? [laughs]

    3. KLKarim R. Lakhani48:51

       A wrapper around a thin-

    4. MMMarina Mogilko48:53

       A wrapper around-

    5. KLKarim R. Lakhani48:53

       Yeah

    6. MMMarina Mogilko48:53

       ... a frontier model-

    7. KLKarim R. Lakhani48:54

       [laughs]

    8. MMMarina Mogilko48:55

       ... with them getting so advanced?

    9. KLKarim R. Lakhani48:57

       Yeah, I think it's such a good question. Um- I don't know the answer to that yet. [laughs]

    10. MMMarina Mogilko49:02

        You know what, um, Greg Brockman told me, president of OpenAI, he told me, like, ask yourself whether your business is improving with a new frontier model or is it disappearing? This is how you figure out your moat and how it's affected

16. 49:14 – 51:34

    Why leaders have to use AI themselves

    1. MMMarina Mogilko49:14

       by the new-

    2. KLKarim R. Lakhani49:14

       Yeah, and I think, and I think you can't outsource that thinking to, uh, somebody else outside of your organization or somebody junior in the organization. You actually have to do that thinking yourself, and it goes back to, look, you have to force yourself to do AI in your work. If you believe my thesis that these models are about expertise, they absorb expertise, and the expertise is, is improving exponentially in these models, and you became a leader in an organization because you have expertise, you have to play with them right now. And I'm reminded of the fact that when email came out in the last century, uh, Stéphane Dion talks about this, I also experienced this, that our bosses' bosses would not do email. They would call in their assistant. They would read the emails to the boss. The boss would then provide the answers back. They would dictaphone this in, then they'd go back to the computer and put that in, and some of those bosses never did email or did the email 10 years later. Same thing's happened with AI, with leaders. "Somebody else's job, not my job. I'm, I'm too busy thinking big thoughts." And let me just... I'll, I'll say one more thing. In talking to CEOs that get it, they're spending between 20... These are of large multinational firms. Those CEOs that get it, they're spending 20 to 30% of their time discussing AI issues. At one global firm, I heard this number, and I'm like, "Are you sure this is right?" They said 50% of senior meetings are talking about, um, the impact of AI on their business and so forth. Just think how profound that has become.

    3. MMMarina Mogilko50:49

       Yeah.

    4. KLKarim R. Lakhani50:50

       And the doing part and the imagination part go hand in hand, and I think Brockman's view is correct. Like, like, you have to be constantly thinking, "It's not just me and my competitor. It's me with AI and the AI itself."

    5. MMMarina Mogilko51:05

       100%. Karim, thank you so much.

    6. KLKarim R. Lakhani51:08

       What a pleasure.

    7. MMMarina Mogilko51:09

       Uh, food for thought, right? We, we all need to be thinking about whether we're wrappers or companies are wrappers, and I think, uh, yeah, it's a question that you keep thinking about every day.

    8. KLKarim R. Lakhani51:17

       I do. I do. Thank you.

    9. MMMarina Mogilko51:19

       Thank you so much.

    10. KLKarim R. Lakhani51:19

        Thanks a lot.

    11. MMMarina Mogilko51:19

        It was amazing. Thank you. If you want to understand what AI is actually doing to jobs, watch my conversation with Stanford economist Erik Brynjolfsson. We get into the research on entry-level work and why some jobs grow even as AI gets better. Click here to watch.

Episode duration: 51:35